The DOI matches its Crossref record.
Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation
Abstract
A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.
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Citations
Cited by
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,544
Data processing for mass spectrometry-based metabolomics
Katajamaa, M., & Orešič, M. (2007) · Journal of Chromatography A · doi:10.1016/j.chroma.2007.04.021 · Cited by 616
Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., Saude, E. J., Lix, B., Adamko, D. J., Shah, S., et al. (2007) · Analytical Chemistry · doi:10.1021/ac0708588 · Cited by 401
Gorrochategui, E., Jaumot, J., Lacorte, S., & Tauler, R. (2016) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2016.07.004 · Cited by 352
Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data
Fan, S., Kind, T., Cajka, T., Hazen, S. L., Tang, W. H. W., Kaddurah-Daouk, R., Irvin, M. R., Arnett, D. K., Barupal, D. K., & Fiehn, O. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.8b05592 · Cited by 336
Metadata
- Authors
- Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., van Ommen, B., & Smilde, A. K.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-12-14
- Volume
- 78
- Issue
- 2
- Pages
- 567-574
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2062551369
- Cited by
- 867
- References
- 0
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., van Ommen, B., & Smilde, A. K. (2006). Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation. Analytical Chemistry. https://doi.org/10.1021/ac051495j
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